Special Issue Information

Dear Colleagues,

Over last few decades, rapid advances in new and maturing geodesy techniques have revolutionized geodesy. Geodesy has grown rapidly and there are crucial geodetic applications in different scientific fields now. We need to summarize the current status of the field and articulate new and emerging research opportunities in geodesy and interdisciplinary applications.

High-resolution topography data and precise geodetic measurements now can be obtained. Since 1994, when GPS achieved its Full Operational Capacity (FOC), Global Navigation Satellite Systems (GNSS) developed quickly. GNSS, including GPS, GLONASS, Compass, and developing Galileo, can provide more accurate measurements. Gravity Recovery and Climate Experiment (GRACE) satellites enable us to observe the movement of mass near the Earth’s surface. Light Detection and Ranging (LiDAR) helps us to obtain extraordinary images of active faults. Using Interferometric Synthetic Aperture Radar (InSAR), we can measure Earth surface deformation. These measurements provide different tools to study Earth in different ways. How to use them efficiently need to be discussed.

Least-Squares Estimation (LSE) is commonly used in geodesy data processing. However, it cannot resist the gross-error in geodetic measurements. Further, many kinds of spatial measurements are not linear. In this situation, LSE may not be the best choice for data processing. Thus, some non-linear models have been proposed, such as Grey Model (GM), neural network model, and Support Vector Machine (SVM). These models sometimes can provide a better precision, especially in prediction. Thus, how to choose an appropriate model in geodetic data processing is one of the most important factors need to be taken into account.

With precise geodetic measurements and new data processing methods, in addition to the science of observing and understanding the Earth’s shape and rotation, geodesy has been used in a wide range of scientific fields. GNSS measurements, accompanied by SAR data, are used to remotely sense the Earth’s surface and atmosphere. Some notable emerging applications include monitoring sea ice, soil moisture, snow depth, sea level change, and smog pollution. What else geodesy can do in our everyday lives? It is an interesting question.

Therefore, though geodesy has achieved notable progress, there are still some challenging problems that need to be addressed. How do we integrate geodetic measurements to meet the requirements of understanding and modelling Earth changes? Which data processing model works well for natural disaster prediction? What is a new research field in geodesy? These are issues that need to be discussed. Selected topics will focus on these aspects.

Special Issue topics:

Overview current geodesy research fields and applications

Data processing methods and their performance

Integration of geodetic measurements

Applications in new research fields

The issue may include, but is not limited to, the above-mentioned topics.

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Web Feature Service (WFS) is a widely used spatial web service standard issued by the Open Geospatial Consortium (OGC). In a heterogeneous GIS application, a user can issue a query that relates two or more spatial datasets at different WFS servers. Multi-way spatial
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Web Feature Service (WFS) is a widely used spatial web service standard issued by the Open Geospatial Consortium (OGC). In a heterogeneous GIS application, a user can issue a query that relates two or more spatial datasets at different WFS servers. Multi-way spatial joins of WFSs are very expensive in terms of computation and transmission because of the time-consuming interactions between the servers and the client. In this paper, we examine the problems of multi-way spatial joins of WFSs, and we present a client-side optimization approach to generate good execution plans for such queries. The spatial semi-join and area partitioning-based methods are combined to prune away non-candidate objects in processing binary spatial joins, and the filtering rate is used as an index to determine the execution strategy for each sub-area. Two partitioning methods were tested, and the experimental results showed that both are effective if a proper threshold to stop the partitioning is chosen. In processing multi-way spatial joins of WFSs, the filtering rate is used as an indicator to determine the ordering of the binary joins. The optimization method is obviously superior to the other two methods when there are adequate spatial objects involved in the join query, or when more datasets are involved in the join query.
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Although the Klobuchar model is widely used in single-frequency GPS receivers, it cannot effectively correct the ionospheric delay. The Klobuchar model sets the night ionospheric delay as a constant, i.e., it cannot reflect temporal changes at night. The observation data of seventeen International
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Although the Klobuchar model is widely used in single-frequency GPS receivers, it cannot effectively correct the ionospheric delay. The Klobuchar model sets the night ionospheric delay as a constant, i.e., it cannot reflect temporal changes at night. The observation data of seventeen International Global Navigation Satellite System Service (IGS) stations within and around China from 2011 provided by the IGS center are used in this study to calculate the Total Electron Content (TEC) values using the Klobuchar model and the dual-frequency model. The Holt–Winters exponential smoothing model is used to forecast the error of the 7th day between the Klobuchar model and the dual-frequency model by using the error of the former six days. The forecast results are used to develop the sophisticated Klobuchar model when no epochs are missing, considering that certain reasons may result in some of the observation data being missing and weaken the relationship between each epoch in practical applications. We study the applicability of the sophisticated Klobuchar model when observation data are missing. This study deletes observation data of some epochs randomly and then calculates TEC values using the Klobuchar model. A cubic spline curve is used to restore the missing TEC values calculated in the Klobuchar mode. Finally, we develop the sophisticated Klobuchar model when N epochs are missing in China. The sophisticated Klobuchar model is compared with the dual-frequency model. The experimental results reveal the following: (1) the sophisticated Klobuchar model can correct the ionospheric delay more significantly than the Klobuchar model; (2) the sophisticated Klobuchar model can reflect the ionosphere temporal evolution, particularly at night, with the correct results increasing with increasing latitude; and (3) the sophisticated Klobuchar model can achieve remarkable correction results when N epochs are missing, with the correct results being nearly as good as that of the dual-frequency model when no epochs are missing.
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Parallel processing in the real-time visualization of three-dimensional Geographic Information Systems (3DGIS) has tended to concentrate on algorithm levels in recent years, and most of the existing methods employ multiple threads in a Central Processing Unit (CPU) or kernel in a Graphics Processing
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Parallel processing in the real-time visualization of three-dimensional Geographic Information Systems (3DGIS) has tended to concentrate on algorithm levels in recent years, and most of the existing methods employ multiple threads in a Central Processing Unit (CPU) or kernel in a Graphics Processing Unit (GPU) to improve efficiency in the computation of the Level of Details (LODs) for three-dimensional (3D) Models and in the display of Digital Elevation Models (DEMs) and Digital Orthphoto Maps (DOMs). The systematic analysis of the task and data characteristics of parallelism in the real-time visualization of 3DGIS continues to fall behind the development of hardware. In this paper, the basic procedures of real-time visualization of urban 3DGIS are first reviewed, and then the real-time visualization pipeline is analyzed. Further, the pipeline is decomposed into different task stages based on the task order and the input-output dependency. Based on the analysis of task parallelism in different pipeline stages, the data parallelism characteristics in each task are summarized by studying the involved algorithms. Finally, this paper proposes a parallel co-processing mode and a collaborative strategy for real-time visualization of urban 3DGIS. It also provides a fundamental basis for developing parallel algorithms and strategies in 3DGIS.
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Fast and reliable ambiguity resolution (AR) has been a continuing challenge for real-time precise positioning based on dual-frequency Global Navigation Satellite Systems (GNSS) carrier phase observation. New GNSS systems (i.e., GPS modernization, BDS (BeiDou Navigation Satellite System), GLONASS (Global Navigation Satellite System), and Galileo) will provide multiple-frequency signals. The GNSS multiple-constellation and multiple-frequency signals are expected to bring great benefits to AR. A new GNSS single-epoch AR method for a short-range baseline based on triple-frequency signals is developed in this study. Different from most GNSS multiple-constellation AR methods, this technique takes advantage of the triple-frequency signals and robust estimation as much as possible. In this technique, the double difference (DD) AR of the triple-frequency observations is achieved in the first step. Second, the triple-frequency carrier phase observations with fixed ambiguities are used with the dual-frequency carrier phase observations to estimate their ambiguity. Finally, to realize reliable GNSS single-epoch AR, robust estimation is involved. The performance of the new technique is examined using 24 hours of GPS/GLONASS/BDS observation collected from a short-range baseline. The results show that single-epoch AR of the GNSS signals can be realized using this new technique. Moreover, the AR of BDS Geostationary Earth Orbit (GEO) satellites’ observations is easier than are those of the Medium Earth Orbit (MEO) and Inclined Geosynchronous Satellite Orbit (IGSO) satellites’ observations.
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This paper proposes a new Asian single site tropospheric correction model called the Single Site Improved European Geostationary Navigation Overlay Service model (SSIEGNOS) by refining the European Geostationary Navigation Overlay Service (EGNOS) model at a single site. The performance of the SSIEGNOS model
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This paper proposes a new Asian single site tropospheric correction model called the Single Site Improved European Geostationary Navigation Overlay Service model (SSIEGNOS) by refining the European Geostationary Navigation Overlay Service (EGNOS) model at a single site. The performance of the SSIEGNOS model is analyzed. The results show that (1) the bias and root mean square (RMS) error of zenith tropospheric delay (ZTD) calculated from the EGNOS model are 0.12 cm and 5.87 cm, respectively; whereas those of the SSIEGNOS model are 0 cm and 2.52 cm, respectively. (2) The bias and RMS error show seasonal variation in the EGNOS model; however, little seasonal variation is observed in the SSIEGNOS model. (3) The RMS error decreases with increasing altitude or latitude in the two models; however, no such relationships were found in the bias. In addition, the annual predicted bias and RMS error in Asia are −0.08 cm and 3.14 cm for the SSIEGNOS model, respectively; however, the EGNOS and UNB3m (University of New Brunswick) models show comparable predicted results. Relative to the EGNOS model, the annual predicted bias and RMS error decreased by 55% and 48%, respectively, for the SSIEGNOS model.
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The Open Geospatial Consortium (OGC) Geography Markup Language (GML) explicitly represents geographical spatial knowledge in text mode. All kinds of fuzzy problems will inevitably be encountered in spatial knowledge expression. Especially for those expressions in text mode, this fuzziness will be broader. Describing
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The Open Geospatial Consortium (OGC) Geography Markup Language (GML) explicitly represents geographical spatial knowledge in text mode. All kinds of fuzzy problems will inevitably be encountered in spatial knowledge expression. Especially for those expressions in text mode, this fuzziness will be broader. Describing and representing fuzziness in GML seems necessary. Three kinds of fuzziness in GML can be found: element fuzziness, chain fuzziness, and attribute fuzziness. Both element fuzziness and chain fuzziness belong to the reflection of the fuzziness between GML elements and, then, the representation of chain fuzziness can be replaced by the representation of element fuzziness in GML. On the basis of vague soft set theory, two kinds of modeling, vague soft set GML Document Type Definition (DTD) modeling and vague soft set GML schema modeling, are proposed for fuzzy modeling in GML DTD and GML schema, respectively. Five elements or pairs, associated with vague soft sets, are introduced. Then, the DTDs and the schemas of the five elements are correspondingly designed and presented according to their different chains and different fuzzy data types. While the introduction of the five elements or pairs is the basis of vague soft set GML modeling, the corresponding DTD and schema modifications are key for implementation of modeling. The establishment of vague soft set GML enables GML to represent fuzziness and solves the problem of lack of fuzzy information expression in GML.
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Multi-constellation global navigation satellite systems (GNSSs) are expected to enhance the capability of precise point positioning (PPP) by improving the positioning accuracy and reducing the convergence time because more satellites will be available. This paper discusses the performance of multi-constellation kinematic PPP based
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Multi-constellation global navigation satellite systems (GNSSs) are expected to enhance the capability of precise point positioning (PPP) by improving the positioning accuracy and reducing the convergence time because more satellites will be available. This paper discusses the performance of multi-constellation kinematic PPP based on a multi-constellation kinematic PPP model, Kalman filter and stochastic models. The experimental dataset was collected from the receivers on a vehicle and processed using self-developed software. A comparison of the multi-constellation kinematic PPP and real-time kinematic (RTK) results revealed that the availability, positioning accuracy and convergence performance of the multi-constellation kinematic PPP were all better than those of both global positioning system (GPS)-based PPP and dual-constellation PPP. Multi-constellation kinematic PPP can provide a positioning service with centimetre-level accuracy for dynamic users.
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Given the chaotic characteristics of the time series of landslides, a new method based on modified ensemble empirical mode decomposition (MEEMD), approximate entropy and the weighted least square support vector machine (WLS-SVM) was proposed. The method mainly started from the chaotic sequence of
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Given the chaotic characteristics of the time series of landslides, a new method based on modified ensemble empirical mode decomposition (MEEMD), approximate entropy and the weighted least square support vector machine (WLS-SVM) was proposed. The method mainly started from the chaotic sequence of time-frequency analysis and improved the model performance as follows: first a deformation time series was decomposed into a series of subsequences with significantly different complexity using MEEMD. Then the approximate entropy method was used to generate a new subsequence for the combination of subsequences with similar complexity, which could effectively concentrate the component feature information and reduce the computational scale. Finally the WLS-SVM prediction model was established for each new subsequence. At the same time, phase space reconstruction theory and the grid search method were used to select the input dimension and the optimal parameters of the model, and then the superposition of each predicted value was the final forecasting result. Taking the landslide deformation data of Danba as an example, the experiments were carried out and compared with wavelet neural network, support vector machine, least square support vector machine and various combination schemes. The experimental results show that the algorithm has high prediction accuracy. It can ensure a better prediction effect even in landslide deformation periods of rapid fluctuation, and it can also better control the residual value and effectively reduce the error interval.
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As the most active plateau on the Earth, the Qinghai-Tibet Plateau (TP) has a complex crust–mantle structure. Knowledge of the distribution of such a structure provides information for understanding the underlying geodynamic processes. We obtain a three-dimensional model of the density of the
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As the most active plateau on the Earth, the Qinghai-Tibet Plateau (TP) has a complex crust–mantle structure. Knowledge of the distribution of such a structure provides information for understanding the underlying geodynamic processes. We obtain a three-dimensional model of the density of the crust and the upper mantle beneath the TP and surrounding areas from height anomalies using the Earth Gravitational Model 2008 (EGM2008). We refine the estimated density in the model iteratively using an initial density contrast model. We confirm that the EGM2008 products can be used to constrain the crust–mantle density structures. Our major findings are: (1) At a depth of 300–400 km, high-D(ensity) anomalies terminate around the Jinsha River Suture (JRS) in the central TP, which suggests that the Indian Plate has reached across the Bangong Nujiang Suture (BNS) and almost reaches the JRS. (2) On the eastern TP, low-D(ensity) anomalies at a depth of 0–300 km and with high-D anomalies at 400–670 km further verified the current eastward subduction of the Indian Plate. The ongoing subduction process provides force that results in frequent earthquakes and volcanoes. (3) At a depth of 600 km, low-D anomalies inside the TP illustrate the presence of hot weak material beneath it, which contribute to the inward thrusting of external material.
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In this study, the characteristics of the total electron content (TEC) fluctuations and their regional differences over China were analyzed by utilizing the rate of the TEC index (ROTI) based on GPS data from 21 reference stations across China during a solar cycle.
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In this study, the characteristics of the total electron content (TEC) fluctuations and their regional differences over China were analyzed by utilizing the rate of the TEC index (ROTI) based on GPS data from 21 reference stations across China during a solar cycle. The results show that there were significant regional differences at different latitudes. Strong ionospheric TEC fluctuations were usually observed at lower latitudes in southern China, where the occurrence of TEC fluctuations demonstrated typical nighttime- and season-dependent (equinox months) features. This phenomenon was consistent with the ionospheric scintillation characteristics of this region. Additionally, compared to low-latitude China, the intensity of TEC fluctuations over mid-latitude China was significantly weaker, and the occurrence of TEC fluctuations was not a nighttime-dependent phenomenon. Moreover, the intensity of TEC fluctuations was much stronger during high solar activity than during low solar activity. Furthermore, the summer-dependent characteristics of TEC fluctuations gradually emerged over lower mid-latitude areas as equinox characteristics weakened. Similar to the equinox characteristics, the summer-dependent characteristics gradually weakened or even disappeared with the increasing latitude. Relevant discussions of this phenomenon are still relatively rare, and it requires further study and analysis.
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Noise filtering, data predicting, and unmonitored data interpolating are important to dam deformation data analysis. However, traditional methods generally process single point monitoring data separately, without considering the spatial correlation between points. In this paper, the Space-Time Kalman Filter (STKF), a dynamic spatio-temporal
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Noise filtering, data predicting, and unmonitored data interpolating are important to dam deformation data analysis. However, traditional methods generally process single point monitoring data separately, without considering the spatial correlation between points. In this paper, the Space-Time Kalman Filter (STKF), a dynamic spatio-temporal filtering model, is used as a spatio-temporal data analysis method for dam deformation. There were three main steps in the method applied in this paper. The first step was to determine the Kriging spatial fields based on the characteristics of dam deformation. Next, the observation noise covariance, system noise covariance, the initial mean vector state, and its covariance were estimated using the Expectation Maximization algorithm (EM algorithm) in the second step. In the third step, we filtered the observation noise, interpolated the whole dam unmonitored data in space and time domains, and predicted the deformation for the whole dam using the Kalman filter recursion algorithm. The simulation data and Wuqiangxi dam deformation monitoring data were used to verify the STKF method. The results show that the STKF not only can filter the deformation data noise in both the temporal and spatial domain effectively, but also can interpolate and predict the deformation for the whole dam.
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Global navigation satellite sensors can transmit three frequency signals. When the classical three-carrier ambiguity resolution (TCAR) is applied to long baselines of hundreds of kilometres, the narrow-lane integer ambiguity resolution (IAR) is affected by the remaining double-differenced (DD) ionospheric delays. As such, large
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Global navigation satellite sensors can transmit three frequency signals. When the classical three-carrier ambiguity resolution (TCAR) is applied to long baselines of hundreds of kilometres, the narrow-lane integer ambiguity resolution (IAR) is affected by the remaining double-differenced (DD) ionospheric delays. As such, large amounts of observational data are typically needed for successful recovery. To strengthen ionospheric delays, we analysed the combination of three frequency signals and a new ambiguity-free ionospheric combination where the least amount of noise is defined, which is enhanced with epoch-differenced ionospheric delays to provide better absolute ionospheric delay and temporal change. To optimize ionosphere estimations, we propose defining the optimal smoothing length, and also propose a strategy to diagnose wrongly determined ionospheric estimations. With such ionospheric information, we can obtain the ionosphere-weighted model by incorporating the ionospheric information to the geometry-based model and use the real triple-frequency observations to evaluate our method. Our results show that the precision of ionospheric estimations from our new ionospheric model is 25% higher than that from the current combination method and that it can provide real-time smoothed ionospheric delay with magnitudes defined to the nearest centimetre. Additionally, using ionospheric estimation as a constraint, the ionosphere-weighted model requires 20% less time to generate the first-fixed solution (TFFS) than the geometry-based model.
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By August 2016, 5 new-generation BeiDou satellites (BeiDou-3) have successfully been launched. The observations of a very limited number of 9 International GNSS (Global Navigation Satellite System) Monitoring and Assessment Service (iGMAS) stations and 52 Multi-GNSS Experiment (MGEX) stations from 16 July to
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By August 2016, 5 new-generation BeiDou satellites (BeiDou-3) have successfully been launched. The observations of a very limited number of 9 International GNSS (Global Navigation Satellite System) Monitoring and Assessment Service (iGMAS) stations and 52 Multi-GNSS Experiment (MGEX) stations from 16 July to 14 August 2016 are processed to determine the orbits of BeiDou-3 and BeiDou-2 satellites, respectively. The internal consistency and satellite laser ranging (SLR) validations are conducted for the orbit validation. BeiDou-3 MEO (Medium Earth Orbit) (C33 and C34) have larger root mean square (RMS) values than those BeiDou-3 IGSO (C31 and C32), whereas BeiDou-2 MEO satellites have smaller RMS values than the BeiDou-2 IGSO satellites. Furthermore, BeiDou-3 IGSO and BeiDou-2 satellites have RMS values at identical levels, whereas BeiDou-3 MEO satellites have larger RMS values than the BeiDou-2 MEO satellites. The RMS residuals are approximately 10 cm in the radial component and approximately 25 cm in the along component for BeiDou-3 IGSO satellites. For BeiDou-3 MEO satellites, the RMS residuals are approximately 40 cm in the radial component and approximately 60 cm in the along component. The SLR validation reports that the orbit radial component can reach an accuracy on the level of 1 decimeter and 4 decimeters for BeiDou-3 IGSO and MEO, respectively.
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Land-cover datasets are crucial for earth system modeling and human-nature interaction research at local, regional and global scales. They can be obtained from remotely sensed data using image classification methods. However, in processes of image classification, spectral values have received considerable attention for
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Land-cover datasets are crucial for earth system modeling and human-nature interaction research at local, regional and global scales. They can be obtained from remotely sensed data using image classification methods. However, in processes of image classification, spectral values have received considerable attention for most classification methods, while the spectral curve shape has seldom been used because it is difficult to be quantified. This study presents a classification method based on the observation that the spectral curve is composed of segments and certain extreme values. The presented classification method quantifies the spectral curve shape and takes full use of the spectral shape differences among land covers to classify remotely sensed images. Using this method, classification maps from TM (Thematic mapper) data were obtained with an overall accuracy of 0.834 and 0.854 for two respective test areas. The approach presented in this paper, which differs from previous image classification methods that were mostly concerned with spectral “value” similarity characteristics, emphasizes the "shape" similarity characteristics of the spectral curve. Moreover, this study will be helpful for classification research on hyperspectral and multi-temporal images.
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Soil moisture plays an important role in understanding climate change and hydrology, and L-band passive microwave radiometers have been verified as effective tools for monitoring soil moisture. This paper proposes a novel, simplified algorithm for bare surface soil moisture retrieval using L-band radiometer.
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Soil moisture plays an important role in understanding climate change and hydrology, and L-band passive microwave radiometers have been verified as effective tools for monitoring soil moisture. This paper proposes a novel, simplified algorithm for bare surface soil moisture retrieval using L-band radiometer. The algorithm consists of two sub-algorithms: a surface emission model and a soil moisture retrieval model. In analyses of the advanced integral equation model (AIEM) simulated database, the surface emission model was developed to diminish the effects of surface roughness using dual-polarization surface reflectivity. The soil moisture retrieval model, which was calibrated using the Dobson simulated database, is based on the relationship between the adjusted real refractive index Nr and the volumetric soil moisture. Soil moisture can be determined via a numerical solution that uses several freely available input parameters: dual-polarization microwave brightness temperature, surface temperature, and the contents of sand and clay. The results showed good agreement with the input soil moisture values simulated by the AIEM model, with root mean square errors (RMSEs) lower than 3% at all incidence angles. The algorithm was then verified based on data from the four-year L-band experiments conducted at Beltsville Agricultural Research Center (BARC) test sites, achieving RMSEs of 4.3% and 3.4% at 40° and 50°, respectively. These results indicate that the simplified algorithm proposed in this paper shows a very good accuracy in soil moisture retrieval. Additionally, the algorithm exhibits a better performance for the large incidence angle radiometers in L-band such as those produced by the Soil Moisture Active and Passive (SMAP).
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